Enhancing Customer Engagement Through RFM-Based Customer Segmentation for a Leading Jewelry Retailer

Solution

• Q3 proposed and implemented RFM analysis for this requirement. This technique helped in identifying key differentiators that divided customers into groups that can be targeted.
• RFM (Recency, Frequency, and Monetary) analysis was used to model for behavior-based customer segmentation.
• It helped in grouping the customers based on their transaction history which included how recently, how often, how much did they buy and many more.
• The success of this model lied in feature engineering or extraction post the data ingestion and exploration on the customers’ historical sales data.
• The cleaned and aggregated data was fed to the clustering algorithm to provide a refined segmentation and refined to reduce the loss function.

Outcome